Presenter: 施佩汝 劉冠廷 何元臣 陳裕美 洪家榮 Approximation Algorithms for Quickest Spanning Tree Problems

Author 2 Refael HassinAsaf Levin

Outline 3 Introduction A 2-approximation algorithm for the quickest radius spanning tree problem Inapproximability of the quickest radius spanning tree problem The quickest diameter spanning tree problem Discussion

施佩汝 Introduction

5 G = (V, E): undirected multi-graph for each e Є E l(e) ≧ 0: length c(e) ＞ 0: capacity r(e) ＝ 1/c(e): reciprocal capacity

Example 6 node l(e) = 1, r(e) = 1l(e) = 1, r(e) = 2 l(e) = 1, r(e) = 1

Introduction 7 For a path P that connects u and v, The length of P: l( P ) = Σ e Є P l(e) The reciprocal capacity of P: max e Є P r(e) t(P) = l(P) + σ r (P): transmission time σ = 1

Example 8 node l(e) = 1, r(e) = 1 l(P) = 2, r(P) = 2, t(P) = 4 l(e) = 1, r(e) = 1l(e) = 1, r(e) = 2

Introduction 9 Quickest Path Problem (QPP) Find a path of given pair of vertices u, v Є V, whose transmission time is minimized

Example 10 u node v l(e) = 1, r(e) = 1l(e) = 1, r(e) = 2 l(e) = 1, r(e) = 1 l(P1) = 2, r(P1) = 2, t(P1) = 4

Example 11 u node v l(e) = 1, r(e) = 1l(e) = 1, r(e) = 2 l(e) = 1, r(e) = 1 l(P1) = 2, r(P1) = 2, t(P1) = 4 l(P2) = 1, r(P2) = 1, t(P2) = 2

Example 12 u node v l(e) = 1, r(e) = 1l(e) = 1, r(e) = 2 l(e) = 1, r(e) = 1 l(P1) = 2, r(P1) = 2, t(P1) = 4 l(P2) = 1, r(P2) = 1, t(P2) = 2

Introduction 13 In broadcast networks, one usually asks for a small maximum delay of a message sent by a root vertex to all the other vertices in the network Quickest Radius Spanning Tree Problem rad t (T) = max v Є V t(P T root,v ) is minimize

Example 14 root node l(e) = 2, r(e) = 0 l(e) = 1, r(e) = 0

Example 15 root node l(e) = 2, r(e) = 0 l(e) = 1, r(e) = 0 rad T = 2

Introduction 16 In other communication networks, one seeks a small upper bound on the delay of transmitting a message between any pair of vertices Quickest Diameter Spanning Tree Problem diam t (T) = max u, v Є V t(P T u,v ) is minimize

Example 17 node l(e) = 2, r(e) = 0 l(e) = 1, r(e) = 0 diam T = 2

Introduction 18 Shortest Paths Tree (SPT) Given an undirected multi-graph G = (V, E), with a special vertex root Є V. e Є E is endowed with a length l(e) ≧ 0 T = (V, E T ): spanning tree such that for every u Є V, l(P T root, u ) = l(P G root, u ) SPT (G, root, l)

Introduction 19 Contribution A 2-approximation algorithm for Quickest Radius Spanning Tree Problem. For any ε > 0, unless P = NP there is no approximation algorithm with performance guarantee of 2 – ε for the Quickest Radius Spanning Tree Problem. A 3/2-approximation algorithm for Quickest Diameter Spanning Tree Problem. For any ε > 0, unless P = NP there is no approximation algorithm with performance guarantee of 3/2 – ε for the Quickest Diameter Spanning Tree Problem.

劉冠廷 A 2-approximation algorithm for quickest radius spanning tree problem

Problem 21 Find a spanning tree T of G such that is minimized.

Algorithm 22

Example 1 root 1 23 l 2, r 0 l 0, r 1 l 2, r 0 G Rad t (T’) = t(root, 1) = 2 23

root Example 2 l 1, r 0 l 0, r 2 l 1, r 1 l 1, r 0 l 1, r 1 G Rad t (T’) = t(root, 3, 4) = 3 24

Time It is dominated by the time complexity of step1 O(m 2 + mn logm), n = |V|, m = |E| Y. L. Chen and Y. H. Chin, “The quickest path problem”,

Theorem 2 The algorithm is a 2-approxiamtion for the QUICKEST RADIUS SPANNING TREE PROBLEM root OPT = l(P) + max r(P) r(e) 26

Theorem 2 root OPT = l(P) + max r(P) l(e) 27

Theorem 2 + ) 28

何元臣 Inapproximability of the quickest radius spanning tree problem

2-approximaiton for quickest radius spanning tree 30 Unless P = NP, no (2- ε ) approximation algorithm exists for any ε > 0. Steps: No approximation algorithm with a performance guarantee of (3/2 – ε ). An example showing quick_radius is a 2-apporixmation algorithm at best. Use ideas from previous two parts to form main result

3/2 lower bound on the approximation ratio 31 Reduction from SAT SAT(Boolean satisfiability problem) Boolean expression written using only AND, OR, NOT, variables and parentheses. Literal = variable An expression is said to be satisfiable if logical values can be assigned to variables to make the formula true. NP-Complete Conjunctive Normal Form (CNF) (X1vX3’vX4’) (X2vX4)

3/2 lower bound on the approximation ratio 32 E3 : l(e) = 0.5, r(e) = 0 E1 : l(e) = 0, r(e) = 1 E2 : l(e) =0.5, r(e) = 0 X1 X2 X3 X4 root leaf X1’ X2’ X3’ X4’ C1 C2 (X1vX3’vX4’) (X2vX4)

3/2 approximation low bound 33 Find Spanning Tree on G If the formula is satisfiable, rad t (T) = 1 If the formula is not satisfiable, rad t (T) ≥ 3/2

Find the Spanning Tree 34 T = (V, E T ), = root to leaf from E 1, all intermediate vertices are false literals (root, li) from E 3, one edge from E 2, a true literal to each clause

Find the Spanning Tree 35 If the formula is satisfiable, rad t (T) = 1 rad(root – leaf) = rad(root – C1) = rad(root – C2) = 1

Find the Spanning Tree 36 If the formula not satisfiable, rad t (T) ≥ 3/2 if the path from root to leaf (P) contains an edge from E 3 (at least one)  rad t (T) ≥ 3/2 otherwise, for some clause C j, all of its literals are in P, rad t (root – C j ) ≥ 3/2

On using only quickest path edges 37 G’: the union of a quickest root – u path in G for all u V. claim: spanning tree T’ of G’ satisfies rad t (T) ≥ (2- ε ) OPT for all T’ of G’. (OPT: optimal solution) G k = 5, δ > 0

On using quickest path edges 38 Two best quickest radius spanning trees on G rad t (T) = δ + 1

On using quickest path edges 39 rad t (T) = 2-(1/k) rad t (T) ≥ 2-(1/k)

On using quickest path edges 40 the approximation ratio then is And we are going to show the bound is tight 2- (1/k) δ+1 ≥ 2 - ε

Theorem 4 If P ≠ NP, then there is no ( 2 - ε )-approximation algorithm for the QUICKEST RADIUS SPANNING TREE PROBLEM for any ε > 0 41 陳裕美

E3 : l(e) = j/k, r(e) = 0 E1 : l(e) = 0, r(e) = 1 E2 : l(e) =1 - j/k, r(e) = 0 root X1j X2j X3j X4j tail j-1 head j tail j headj+1 X1’j X2’j X3’j X4’j C1j C2j (X1vX3’vX4’)^(X2vX4) Level j 42

rad t (T) = 1 (the optimal case) 43

44 E3 : l(e) = j/k, r(e) = 0 E2 : l(e) =1 - j/k, r(e) = 0 E1 : l(e) = 0, r(e) = 1 SAT: X1 = false, X2 = true, X3 = true, X4 = false root X1j X2j X3j X4j tail j-1 head j tail j headj+1 X1’j X2’j X3’j X4’j C1j C2j (X1vX3’vX4’)^(X2vX4)

rad t (T) = 1 45 Root (head 1 ) to tail k-1 : E1 : l(e) + r(e) = = 1 Root to C ij : E2 + E3 = j/k + (1 – j/k) = 1

rad t (T’) ≠ 1, we want to prove rad t (T’) ≥ 2-1/k 46

E3 : l(e) = j/k, r(e) = 0 E1 : l(e) = 0, r(e) = 1 E2 : l(e) =1 - j/k, r(e) = 0 root X1j X2j X3j X4j tail j-1 head j tail j headj+1 X1’j X2’j X3’j X4’j C1j C2j (X1vX3’vX4’)^(X2vX4) 47

If the SAT formula is not satisfied 48 Look at the path from head to tail For every level j = 1,.., k-1, (V, E T ∩ E 1 ) does not contain a path from head j to tail j case I There is some level j, 1 ≤ j ≤ k-1, (V, E T ∩ E 1 ) does not contain a path from head j to tail j case II Look at the path from root to C ij ----case III

But before the proof.. Claim: for every q = 1,2,…..,j, (V, E T ∩ E 1 ) does not contain a path from head q to tail q, then l(P t root,tail j ) ≥ j/k. 49

Proved by induction 50 For j = 1, the claim is trivial since P t root,tail j must have an edge from E2 ∪ E3 Assume that the claim holds for all previous level, we prove it for j.

Proved by induction 51 By assumption, (V, E T ∩ E 1 ) does not contain a tail j – head j path. – Has an edge from E 3 l(P t root,tail j ) ≥ j/k – Has two edges from E 2 l(P t tail j-1,tail j ) ≥ 2/k and add l(P t root,tail j ) 2/k + (j-1)/k = (j+1)/k > j/k E3 : l(e) = j/k, r(e) = 0 E2 : l(e) =1 - j/k, r(e) = 0

Now back to the proof!! rad t (T’) ≠ 1, we want to prove rad t (T’) ≥ 2-1/k 52

If the SAT formula is not satisfied 53 Look at the path from head to tail For every level j = 1,.., k-1, (V, E T ∩ E 1 ) does not contain a path from head j to tail j case I There is some level j, 1 ≤ j ≤ k-1, (V, E T ∩ E 1 ) does not contain a path from head j to tail j case II Look at the path from root to C ij ----case III

Case I For every level j = 1,.., k-1, (V, E T ∩E 1 ) does not contain a path from head j to tail j. 54 By claim– for every q = 1,2,…..,j, (V, E T ∩ E 1 ) does not contain a path from head q to tail q, then l(P t root,tail j ) ≥ j/k. Because now j = k-1: l(P t root,tail k-1 ) ≥ k-1/k = 1 – 1/k

Case II There is some level j, 1 ≤ j ≤ k-1, (V, E T ∩E 1 ) does not contain a path from head j to tail j. 55 WLOG suppose 1,…, j-1 does not contain a path form head j to tail j. otherwise j,…, k-1 contains a path (V, E T ∩ E 1 ) from head j to tail j. For j ≥ 2, By claim for j-1, l(P t root,tail j-1 ) ≥ (j-1)/k. – If (tail j-1,head j ) is E T, then l(P t root,tail j-1 ) ≥ (j-1)/k. – If (tail j-1, head j )is from E 3, then it connects a vertex from a level at least j to root. l(P t root,tail j-1 ) = j/k ≥ (j-1)/k. If j = 1, then l(P t root,tail j ) ≥ (j-1)/k

Case I + Case II 56 We have l(P t root,tail j-1 ) ≥ (j-1)/k in E 1. E3 : l(e) = j/k, r(e) = 0 E1 : l(e) = 0, r(e) = 1 E2 : l(e) =1 - j/k, r(e) = 0 root X1j X2j X3j X4j tail j-1 head j tail j headj+1 X1’j X2’j X3’j X4’j C1j C2j (X1vX3’vX4’)^(X2vX4)

Case III Look at the path from root to C ij 57 Since the formula is not satisfied by this truth assignment, there is a clause vertex C jp such that all of its neighbors are in E 1. E3 : l(e) = j/k, r(e) = 0 E1 : l(e) = 0, r(e) = 1 E2 : l(e) =1 - j/k, r(e) = 0 root X1j X2j X3j X4j tail j-1 head j tail j headj+1 X1’j X2’j X3’j X4’j C1j C2j (X1vX3’vX4’)^(X2vX4)

Case III 58 l(P t root,C jp ) = (1 – j/k ) + (j-1)/k = 1 – 1/k t(e) = l(e) + r(e) = 1 - 1/k + 1 = 2 – 1/k Therefore, rad t (T’) > = 2 – 1/k

洪家榮 The quickest diameter spanning tree (QDST) problem

In QDST problem 60 In this section we consider the quickest diameter spanning tree problem. We present a 3/2-approximation algorithm, and prove that unless P = NP this is the best possible. Denote by OPT the cost of an optimal solution, Topt = (V,Eopt), to the quickest diameter spanning tree problem. Denote by MDT(G, l) the minimum diameter spanning tree of a graph G where the edges are endowed with a length function l.

The algorithm 61

Proof: The claim clearly holds if the path contains an edge e with. Assume otherwise (that the claim does not hold), and let be such that there exists and contains an edge e with, and are edge-disjoint. Then, and the claim follows. For a tree T, denote by uvy z Lemma 5 Let. For every pair of vertices

Theorem 6 Algorithm quick diameter is a 3/2-approximation algorithm for the quickest diameter spanning tree problem. 63 Proof: 1.By the optimality of (for the minimum diameter spanning tree problem), 2. By Lemma 5, Therefore, by 1, 3. Since 4. By 2 and 3, 5. By 1, 6. By 5, 7. By 4 and 6,

Theorem 7 If P≠NP, then there is no (3/2 -ε)-approximation algorithm for the quickest diameter spanning tree problem for any ε> Proof: We prove the claim via reduction from SAT. We take the graph G from the proof of Theorem 4, and add a new vertex leaf, that is adjacent only to root by an edge e with l(e) = 1 and r(e) = 0. By the proof of Theorem 4, if the formula can be satisfied, then there is a tree, T, whose radius is 1 (by extending the tree derived in the proof of Theorem 4 with the edge (root, leaf)). The diameter of a tree is at most twice its radius. Therefore, there is a spanning tree T such that

Since leaf is adjacent (in G) only to root, each feasible solution is decomposed into a spanning tree over the original vertex set and the edge (root, leaf). By the proof of Theorem 4, if the formula cannot be satisfied, then every spanning trees T has a vertex u (u ≠ leaf) such that and. Therefore, Given ε > 0 we can pick an integer k such that 65

leaf (l, r) = (1, 0) 66

施佩汝 Discussion

68 Consider the transmission time along paths instead of the usual length of the path. There are numerous other graph problems of interest that ask to compute a minimum cost subgraph where the cost is a function of the distances among vertices on the subgraph. Defining these problems under the transmission time definition of length opens an interesting area for future research.

Thank You 69

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71 洪家榮 何元臣 施佩汝 劉冠廷 陳裕美 ( 左到右 )

圖論演算法 … 72

讓人心曠神怡 73